Papers Few-Shot Object Detection
“Few-Shot Object Detection” 태그가 달린 논문 189편 · 필터 해제
Personalized Object Identification and Localization via In-Context Inference with Vision-Language Models
Personalized object localization (POL) localizes an object instance in a query image based on a few reference images with bounding-box annotations and a target object label. The pioneering method, IPLoc, solves this task…
Few-Shot Object DetectionObject LocalizationRethinking Prototype-based Similarity Learning for Few-Shot Object Detection
Few-shot object detection aims to detect novel object categories from only a few labeled examples, avoiding costly large-scale annotation. Recent prototype-based similarity learning approaches enable training-free adapta…
Few-Shot Object DetectionProposal Refinement for Few-Shot Object Detection
Few-shot object detection has gained widely attention in recent years. Some excellent algorithms have been proposed to handle this task. However, most of these algorithms rely on the performance of few-shot classificatio…
Few-Shot Object DetectionDecoupled Prototype Matching with Vision Foundation Models for Few-Shot Industrial Object Detection
Industrial object detection systems typically rely on large annotated datasets, which are expensive to collect and challenging to maintain in industrial scenarios where the inventory of objects changes frequently. This w…
Few-Shot Object Detection2D Object DetectionPose EstimationDetPO: In-Context Learning with Multi-Modal LLMs for Few-Shot Object Detection
Multi-Modal LLMs (MLLMs) demonstrate strong visual grounding capabilities on popular object detection benchmarks like OdinW-13 and RefCOCO. However, state-of-the-art models still struggle to generalize to out-of-distribu…
Few-Shot Object DetectionVisual GroundingFSOD-VFM: Few-Shot Object Detection with Vision Foundation Models and Graph Diffusion
In this paper, we present FSOD-VFM: Few-Shot Object Detectors with Vision Foundation Models, a framework that leverages vision foundation models to tackle the challenge of few-shot object detection. FSOD-VFM integrates t…
Few-Shot Object DetectionIn-Context Adaptation of VLMs for Few-Shot Cell Detection in Optical Microscopy
Foundation vision-language models (VLMs) excel on natural images, but their utility for biomedical microscopy remains underexplored. In this paper, we investigate how in-context learning enables state-of-the-art VLMs to …
Few-Shot Object DetectionCell DetectionPrototype-Driven Adaptation for Few-Shot Object Detection
Few-shot object detection (FSOD) often suffers from base-class bias and unstable calibration when only a few novel samples are available. We propose Prototype-Driven Alignment (PDA), a lightweight, plug-in metric head fo…
Few-Shot Object DetectionFew-Shot Object Detection via Spatial-Channel State Space Model
Due to the limited training samples in few-shot object detection (FSOD), we observe that current methods may struggle to accurately extract effective features from each channel. Specifically, this issue manifests in two …
Few-Shot Object DetectionZERO: Industry-ready Vision Foundation Model with Multi-modal Prompts
Foundation models have revolutionized AI, yet they struggle with zero-shot deployment in real-world industrial settings due to a lack of high-quality, domain-specific datasets. To bridge this gap, Superb AI introduces ZE…
Few-Shot Object DetectionNo time to train! Training-Free Reference-Based Instance Segmentation
The performance of image segmentation models has historically been constrained by the high cost of collecting large-scale annotated data. The Segment Anything Model (SAM) alleviates this original problem through a prompt…
Cross-Domain Few-Shot Object DetectionFew-Shot Object DetectionImage SegmentationInstance Segmentation+2Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances. The existing methods severely suffer from bias…
Few-Shot Object DetectionInstance Segmentationobject-detectionObject Detection+1CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion
Cross-domain few-shot object detection (CD-FSOD) aims to detect novel objects across different domains with limited class instances. Feature confusion, including object-background confusion and object-object confusion, p…
Cross-Domain Few-ShotCross-Domain Few-Shot Object DetectionFew-Shot Object DetectionObject+2NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection and few-shot detection models when applied across domains. In conjunction with NTIRE 2025, we organized the 1st C…
Cross-Domain Few-ShotCross-Domain Few-Shot Object DetectionFew-Shot Object DetectionObject+3Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection
The objective of few-shot object detection (FSOD) is to detect novel objects with few training samples. The core challenge of this task is how to construct a generalized feature space for novel categories with limited da…
Contrastive LearningcounterfactualCounterfactual ExplanationFew-Shot Object Detection+2Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detection
Foundation models pretrained on extensive datasets, such as GroundingDINO and LAE-DINO, have performed remarkably in the cross-domain few-shot object detection (CD-FSOD) task. Through rigorous few-shot training, we found…
Cross-Domain Few-ShotCross-Domain Few-Shot Object DetectionData AugmentationDomain Generalization+5Multimodal Reference Visual Grounding
Visual grounding focuses on detecting objects from images based on language expressions. Recent Large Vision-Language Models (LVLMs) have significantly advanced visual grounding performance by training large models with …
Few-Shot Object DetectionVisual GroundingContext in object detection: a systematic literature review
Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and e…
Few-Shot Object DetectionObjectobject-detectionObject Detection+4Exploring Few-Shot Object Detection on Blood Smear Images: A Case Study of Leukocytes and Schistocytes
The detection of blood disorders often hinges upon the quantification of specific blood cell types. Variations in cell counts may indicate the presence of pathological conditions. Thus, the significance of developing pre…
Few-Shot Object Detectionobject-detectionObject DetectionVisual-RFT: Visual Reinforcement Fine-Tuning
Reinforcement Fine-Tuning (RFT) in Large Reasoning Models like OpenAI o1 learns from feedback on its answers, which is especially useful in applications when fine-tuning data is scarce. Recent open-source work like DeepS…
Few-Shot Object DetectionFine-Grained Image Classificationimage-classificationImage Classification+5